Economics and Technical Efficiency of Maize Production Among Small Scale Farmers in Abuja, Nigeria: Stochastic Frontier Model Approach

Abstract

This study evaluated Economics and Technical Efficiency of Maize Production Among Small Scale Farmers in Abuja, Nigeria; Stochastic Frontier Model Approach; Multi-stage sampling technique was adopted and used. Primary data were collected through the use of well-structured questionnaire from one hundred and fifty (150) sampled respondents in the study area. Data were analyzed to achieve the specified objectives using the following tools of analysis, Descriptive Statistics, Stochastic Frontier, Gross Margin Analysis, Financial Analysis, Five Point Likert Scale, and Principal Components Analysis. The results show that majority 40.47% of the sampled respondents were among the age range of 41-50 years of age and 25.33% were within the age bracket of 31-40 years of age. The mean age of the sampled farmers is 44 years. Which indicated that Young and energetic farmers are able to withstand stress and adopt new innovations. Most 88.67% of the farmers were male. Majority 72.67% were married. The mean household size of the sampled respondents is 6 members per family. Majority (67%) of the small-scale maize farmers had 1-2 ha of land and the average farm size cultivated by the farmers was 2.576 ha. The factors influencing total output of maize were seed input (P<0.01), Farm Size (P<0.05), quantity of fertilizer (P<0.05), and labour input (P<0.01) were statistically significant variables. The results of the inefficiency components show that the factors influencing technical inefficiency includes: age (P<0.01), education (P<0.01), access to credit (P<0.05), farming experience, and household size (P<0.05) and were statistically significant. The total revenue realized by the sampled small-scale maize farmers was N238,317 on average. While the total variable costs on average basis was N109,702.93. The gross margin realized was N128667.07. The gross margin ratio was 0.54 while operating ratio calculated was 0.857 and the rate of return on investment (RORI) was 1.17. The retained component from the constraint’s analysis include Lack of Improve Seed, Lack of Transportation, Poor Storage Facilities, Inadequate Capital, and Cattle Herdsmen. The retained components explained 60% of the variation of the component included in the model. The study therefore, recommends that affordable loans and adequate capital should be made available to the small-scale maize farmers, farmers should be properly trained and educated by the non-governmental organizations and extension agents on chemical application, fertilizer use and pests and diseases control measures especially on integrated pest management (IPM). Inputs like improve seeds, fertilizer, and chemicals should be made available to farmers at subsidized rates and at appropriate time coinciding with production periods for proper usage. Provisions should be made for transportation and storage facilities for farmers, the issue of conflict between farmers and herdsmen should be brought to book and resolved amicably for peaceful coexistence.

Resource use efficiency and factors influencing maize production in Kuje Area Council, Federal Capital Territory, Nigeria

“…Education level of a farmer could determine how efficient the farmer would be, especially in the area of resource utilization which could lead to output and profit maximization. This result is in line with (Alabi et al, 2020 andEbukiba et al, 2020) who reported that education is an important factor that can influence small-scale farmers to adopt new innovations and research findings related to their area of production. When a farmer is educated, there is high probability that he will take advantages of innovations and new technologies easily which would eventually lead to improved yield and increase in output.…”

Profit Efficiency of Soybean Production in Federal Capital Territory, Nigeria

“…Farm size is considered as a proxy in production as the size of the farm land expands will lead to increase in the total output of soybeans in the study area. This is in consonance with [21] and [32] who indicated positive effect of farm size on the output of soybean, they reported that an increase in farm size will results in increase in the total output of soybean. The coefficient fertilizer influence soybean output negatively and was statistically significant at (P<0.05) probability level the coefficient of fertilizer (-1.691) implies that a percentage change in the quantity of fertilizer applied to the soybean farm will results in the 1.69% decrease in the total output of soybean in the study area this findings is contrary to the apriori expectation and the economic theory, this could be because farmers wrongly applied the fertilizer or the soybean itself doesn't require fertilizer because it has the ability to fix nitrogen into the soil, this result is not consistent with [12] who reported positive relationship between fertilizer and output of soybean implying that an increase in fertilizer usage will result in increase in total output of soybean.…”

“…The following type of financial ratios was applied in this study in order to determine the profitability of soybean production by farmers as used by [21] and [22]. This was used to achieve part of specific objective two (ii):…”

A Stochastic Frontier Analysis of Maize Production Efficiency and Its Climate-Smart Agricultural Practice Determinants in Southeast, Nigeria

“…Some farms may be operating significantly below the average efficiency level, indicating potential areas for improvement. This result on mean TE is in higher than the 0.620 reported in Umaru and Maurice (2019) and little lower than the 0.950 reported in Ebukiba et al (2020) [26] .…”

“…Afolami and Faleye (2020) [6] as well as Kalu and Mbanasor (2023) [32] delved into the adoption dynamics of CSAPs, revealing that farm size, age, access to credit, education level, and ownership of means of transport are influential factors. Ebukiba et al (2020) [26] conducted a study focusing on maize productivity, finding a mean technical efficiency (TE) value of 0.950, indicating a high level of efficiency in their sample. Abubakar and Onwujioba (2023) explored various efficiency metrics, reporting mean efficiencies of 97.5% for TE, 46.5% for allocative efficiency (AE), and 45.0% for economic efficiency (EE).…”